The cloud paradigm has emerged as a key enabler for various sectors of industry. Benefits such as cost reductions, flexibility, and scalability continue to increase demands for cloud-based services with forecasts predicting that global cloud and datacentre traffic will increase nearly four-and-a-half-fold and three-fold respectively within the next five years. Global cloud and datacentre traffic are expected to reach 5.3 and 7.7 zettabytes (1 zB=1021 Bytes) respectively by 2017, which is more than five times forecasted global internet protocol traffic for the same period. The mitigation of congestion and efficient utilisation of available resources will become increasingly important as the volume of such traffic increases, and as networked applications become more demanding of data capacity and computing power.
Usage-based pricing has become a common approach for managing demand for networked applications and services using wireless and mobile communications. With usage-based pricing, a service provider offers different capacity allowances (resource and/or data capacity) at fixed prices and a customer agrees to a service level agreement specifying a maximum capacity allowed within a specific time period. This approach usually includes a relatively high fee for exceeding this capacity allowance. Such fees deter customers from exceeding capacity allowances specified in their service level agreements, but do not prevent multiple customers from simultaneously accessing available computation, storage, and network resources, causing congestion at times of peak demand. If a service provider makes available computation, storage, and network resources sufficient to effectively mitigate congestion during peak periods, these resources will be under-utilised for much of the time, resulting in low resource utilization and high cost of providing resources during off-peak periods in excess of the traffic on offer at those times.
Time-dependent pricing is an emerging alternative to usage-based pricing and is based on the concept that customers are offered prices which are computed using not only capacity allowances but also taking into consideration the state of the networked infrastructure when services are to be delivered to customers. Service providers aim to offer relatively lower prices during off-peak periods and incentivise customers to move their demands for networked services to less congested periods. This movement of demand allows a reduction in the volume of resources that are required, as congestion during peak periods is distributed to less congested periods; this in turn will lead to increased resource utilization during off-peak periods and reductions in the cost per user of operating resources during those off-peak periods. It can also allow delivery of networked services to customers whose demands could have been blocked, or who experience signal quality impairment, due to insufficient resource during peak periods.
It is known to provide time-dependent prices for mobile Internet customers; however, these systems merely provide an incentive to a user to decide when to use, or refrain from using, the network resources by imposing a price penalty of peak rate use to encourage the user to operate at cheaper, less busy, times. There is no dynamic placement of computing and storage services. Any change in operational time is entirely under the control of the user, influenced by the pricing system, and there is little opportunity to fine-tune the system to optimise the usage of the resources beyond a few simple and non-dynamic tariff bands that can be readily understood by the users.
It is desirable to use time-shifting to automatically flatten the temporal demand fluctuation between peak and off-peak periods, increasing overall resource utilization, and maximize the efficiency with which those resources are used. The ability to automatically make intelligent decisions on network path selection, computing capability and storage placements in addition to dynamically generating time-dependent prices allows a more optimized end-to-end solution
Applications which are interactive, or require real-time delivery of transactional networked services, are time-critical and cannot be shifted. For this class of applications, the principal QoS (Quality of Service) requirements are response time and throughput. Time-shifting can be applied to customer applications that are time-elastic (i.e. delay-tolerant) requests for non-interactive and non-critical networked services, such as cloud-based services for data synchronization (updating a duplicate data store to match a master data store), data archiving, machine-to-machine (M2M) applications such as networked sensors and meters, and applications for scientific simulations and modelling. The principal quality requirements for such customer applications are not time-dependant and this enables time shifting from peak periods to less congested periods within the acceptable tolerance limits.